Wyniki wyszukiwania - BazTech

1

Badanie optymalizacji procedury przygotowania nowej formulacji karbamazepiny

Ting Chen, Ling Gong, Xianfeng Hou

Przemysł Chemiczny

|

2023

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T. 102, nr 12

1360--1363

PL

Karbamazepina została zaadsorbowana na stałych nanocząstkach lipidowych w celu przygotowania skutecznej formulacji leku do leczenia padaczki. Mieszaninę karbamazepiny oraz monostearynianu glicerolu, Tweenu 60 i Poloxameru poddano sonifikacji w celu przygotowania preparatu karbamazepiny.

EN

Carbamazepine was adsorbed on solid lipid nanoparticles to prep. an efficient drug formulation for epilepsy treatment. A mixt. of carbamazepine-glycerol monostearate, Tween 60 and Poloxamer was sonificated for prepn. of the carbamazepine formulation.

2

Performance analysis of data fusion methods applied to epileptic seizure recognition

Ludwig Simone A.

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 1

5--17

EN

Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.

3

A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network

Kołodziej Marcin, Majkowski Andrzej, Tarnowski Paweł, Rak Remigiusz Jan, Rysz Andrzej

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 3

art. no. e136921

EN

Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.

4

Variational mode decomposition-based seizure classification using Bayesian regularized shallow neural network

Yadav Vipin Prakash, Sharma Kamlesh Kumar

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

402--418

EN

This work presents a new epileptic seizures epoch classification scheme. Variational mode decomposition (VMD), has been explored for non-recursively decomposing the electroencephalogram (EEG) signals into fourteen band limited intrinsic mode functions (IMFs). Data augmentation (DA), has been used for handling unbalanced classification problem. Normalized energy, fractal dimension, number of peaks, and prominence parameters were computed from the band-limited IMFs for the discrimination of seizure and non-seizure epochs. Bayesian regularized shallow neural network (BR-SNNs) and six other well-known classifiers were tested. Sensitivity, specificity, and accuracy have been used as performance metrics. This study includes two different epoch lengths of 1-second and 2-seconds. A total of 32 test cases for both, class balanced and unbalanced classification problems have been taken for the performance evaluation. The best performance obtained is 100% for all the three metrics from the test cases of database-2 and 3. For database-1, average sensitivity, specificity, and accuracy of 99.71, 99.75, and 99.73% have been achieved, respectively for the 1-second epoch. The presented work shows better performance results compared to many previously reported works.

5

Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum

Karabiber Cura Ozlem, Akan Aydin

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

28--44

EN

Dynamic mode decomposition (DMD) is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. Recently, DMD algorithm has successfully been applied to the analysis of non-stationary signals such as neural recordings. In this study, we propose single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. We investigate the possibility of utilizing the ‘‘DMD Spectrum’’ for the classification of pre-seizure and seizure EEG segments. We introduce higher-order DMD spectral moments and DMD sub-band powers, and extract them as features for the classification of epileptic EEG signals. Experiments are conducted on multi-channel EEG signals collected from 16 epilepsy patients. Single-channel, and multi-channel EEG based DMD approaches have been tested on epileptic EEG data recorded from only right, only left, and both brain hemisphere channels. Performance of various classifiers using the proposed DMD-Spectral based features are compared with that of traditional spectral features. Experimental results reveal that the higher order DMD spectral moments and DMD sub-band power features introduced in this study, outperform the analogous spectral features calculated from traditional power spectrum.

6

Pomiary wolumetryczne hipokampa w padaczce na podstawie rezonansu magnetycznego

Kurowska Klaudia, Szymerkowski Karol, Waghorn Ben, Kimszal Ewelina

Inżynier i Fizyk Medyczny

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2019

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Vol. 8, nr 4

321--322

PL

W przypadku pacjentów z padaczką płata skroniowego, która jest oporna na terapię medyczną, najlepszą opcją uzyskania redukcji lub całkowitego zniwelowania napadów jest resekcja chirurgiczna – zwłaszcza gdy w wyniku obrazowania rezonansu magnetycznego stwierdzony jest zanik hipokampa. Zazwyczaj większość badań klinicznych z wykorzystaniem obrazowania rezonansu magnetycznego jest wystarczająca do wykrycia dużego zaniku jego struktury. Niekiedy jednak subtelny zanik tkanek, który może charakteryzować wczesną chorobę, jest często pomijany w ocenie uzyskanego obrazu. Wolumetria hipokampa jest bardzo dobrym markerem do wykrywania obecności zaniku tkanek i stopnia nasilenia tego procesu. W badaniu morfometrii (pomiarze danego narządu oraz jego opisu na podstawie uzyskanych pomiarów) mózgu przyjmuje się, iż istnieje ścisła korelacja pomiędzy jego strukturą oraz funkcją. Dotyczy to zarówno tkanki normalnej (niezmienionej chorobowo), jak i nieprawidłowej (o budowie patologicznej). Jednym z deskryptorów struktury morfometrycznej jest objętość.

EN

In the case of patients with temporal lobe epilepsy, which is resistant to medical therapy, surgical resection is the best option for achieving reduction or complete suppression of seizures – especially when the disappearance of the hippocampus is detected by magnetic resonance imaging. Typically, most clinical trials using magnetic resonance imaging are sufficient to detect a large disappearance of its structure. Sometimes, however, the subtle disappearance of tissues that can characterize early disease is often overlooked in the assessment of the image obtained. Hippocampal volumetry is a very good marker to detect the presence of tissue loss and the extent of this process. In the study of morphometry (measurement of a given organ and its description on the basis of obtained measurements), the brain assumes that there is a close correlation between its structure and function. This applies to both normal (unchanged) and abnormal (pathological) tissue. One of the descriptors of the morphometric structure is the volume.

7

Fast statistical model-based classification of epileptic EEG signals

Quintero-Rincón A., Pereyra M., D'Giano C., Risk M., Batatia H.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 4

877--889

EN

This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using awavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straight-forward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.

8

Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis

Ibrahim S., Djemal R., Alsuwailem A.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

16--26

EN

Quantification of abnormality in brain signals may reveal brain conditions and pathologies. In this study, we investigate different electroencephalography (EEG) feature extraction and classification techniques to assist in the diagnosis of both epilepsy and autism spectrum disorder (ASD). First, the EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). Two nonlinear methods were studied, namely, Shannon entropy and largest Lyapunov exponent, which measure complexity and chaoticity in the EEG recording, in addition to the two conventional methods (namely, standard deviation and band power). We also study the use of a cross-correlation approach to measure synchronization between EEG channels, which may reveal abnormality in communication between brain regions. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design exploration techniques: the University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and our own EEG recordings (46 subjects). The combination of DWT, Shannon entropy, and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem. The proposed method obtained better classification accuracy compared to the existing methods and tested using larger and more comprehensive EEG dataset. The proposed method could potentially be used to assist epilepsy and ASD diagnosis therefore improving the speed and the accuracy.

9

Rezonans magnetyczny w diagnozowaniu padaczki

Kurowska K., Kimszal E.

Inżynier i Fizyk Medyczny

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2017

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Vol. 6, nr 5

315--317

PL

Epilepsja jest chorobą neurologiczną coraz częściej obserwowaną wśród populacji ludzkiej na całym świecie. Etiologia padaczki jest zróżnicowana i często ciężko ustalić przyczynę pojawienia się napadów drgawkowych. Napady są powracające oraz przyjmują różną formę, np. zaburzenia ruchu i świadomości. Wyróżnia się kilka rodzajów napadów: proste i złożone. Definicja napadu padaczkowego mówi, iż jest to bardzo dynamiczny incydent, na który składają się miejscowe i odległe objawy zależne od obszarów mózgu, w których nastąpiło zaburzenie. Przyczyny wystąpienia epizodów drgawkowych różnią się w zależności od wieku chorego. Ogromną rolę w diagnozowaniu padaczki stanowi szereg badań, w tym także badania obrazowe. Jednym z najdokładniejszych dostępnych obecnie badań obrazowych jest rezonans magnetyczny. Badanie to pozwala na dokładny wgląd w tkanki ludzkiego ciała, a w szczególności w tkanki mózgu, gdzie zazwyczaj obserwowane są ogniska padaczkowe. Rezonans wykorzystuje do obrazowania pole magnetyczne, jest on więc zdecydowanie bezpieczniejszym badaniem dla pacjenta niż te, które do obrazowania ludzkiego ciała wykorzystują promieniowanie rentgenowskie, czyli promieniowanie X. W badaniu wykorzystuje się różne sekwencje, które pozwalają na uzyskanie dokładniejszych obrazów badanego obszaru. W przypadku podejrzenia wystąpienia padaczki u pacjenta stosuje się specjalny protokół, który pozwala na zwiększenie skuteczności w wykrywaniu padaczki do 72%. W protokole tym zastosowane są różne sekwencje w różnych przekrojach oraz o różnych grubościach warstw.

EN

Epilepsy is a neurological disease increasingly common among the human population worldwide. The etiology of epilepsy varies and it is often difficult to determine the cause of seizures. These seizures are recurrent and they take on various forms, such as movement or consciousness disorders. There are several types of seizures: simple or complex. The definition of epileptic seizure says that this is a very dynamic incident, which consists of localized and distant symptoms depending on areas of the brain where the disorder occurred. Causes of episodes of seizures vary depending on the patient’s age. An important role in the diagnosis of epilepsy is a number of studies including imaging studies. One of the most accurate imaging studies currently available is magnetic resonance imaging. This study allows for accurate insight into the tissues of the human body, and in particular the brain tissue, where epileptic foci are usually observed. Resonance uses magnetic fields for imaging, so it is definitely safer to test for a patient than to use X-rays for X-rays. The study uses a variety of sequences to produce more accurate images of the area under investigation. In the case of suspected epilepsy, a special protocol is used in the patient, which increases the effectiveness of detecting epilepsy to 72%. This protocol uses different sequences in different cross sections and different layer thicknesses.

10

Dobór funkcji bazowych do aproksymacji sygnału elektrokortygraficznego w detekcji obszaru padaczkorodnego

Kołodziej M., Majkowski A., Rak R. J., Rysz A.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2017

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Vol. 23, nr 4

253--260

PL

Celem badań opisanych jest dobór funkcji bazowych, które pozwolą na dokładne opisanie sygnałów elektrokortykograficznych (ECoG) z zachowaniem ich właściwości diagnostycznych. Sygnały ECoG są powszechnie wykorzystywane do wskazania miejsca obszaru padaczkorodnego mózgu. Do doboru najlepszej rodziny falek w charakterze funkcji bazowych zastosowano algorytm MP (ang. Matching Pursuit). Przedstawiono przykład, w jaki sposób z wykorzystaniem analizy falkowej można wykryć zapisy patologiczne w sygnale ECoG.

EN

The purpose of this research is a selection of base functions, which allow to accurately describe the electrocortical signals (ECoG), while maintaining their diagnostic properties. ECoG signals are commonly used to indicate the epileptogenic zone in the brain. The Matching Pursuit algorithm was used to select the best wavelet family as a base function. An example of using wavelet analysis to detect pathological records in ECoG signal, is demonstrated.

11

Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals

Djemili R., Bourouba H., Korba M. C. A.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

285--291

EN

Epilepsy is a neurological disorder affecting more than 50 million individuals in the world. Analysis of the electroencephalogram (EEG) is a powerful tool to assist neurologists for diagnosis and treatment. In this paper a new feature extraction method based on empirical mode decomposition (EMD) is proposed. The EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and four statistical parameters are calculated over these IMFs constituting the input feature vector to be fed to a multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the publicly available Bonn dataset show that an accurate classification rate of 100% is achieved in the discrimination between normal and ictal EEG, and an accuracy of 97.7% is reached in the classification of interictal and ictal EEG signals. Our results are equivalent or outperform recent studies published in the literature.

12

An accident in Izbucul De La Tauz

Bolek W., Siermontowski P., Dziewiatowski K., Horia C. D., Morawiec B.

Polish Hyperbaric Research

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2013

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nr 4(45)

53--68

EN

The paper describes a deadly accident involving an amateur diver during a cave dive as well as the medical investigation into the causes of the accident. The article points to hazards relating to the lack of medical qualifications of amateur divers.

PL

W pracy opisano śmiertelny wypadek płetwonurka amatora podczas nurkowania jaskiniowego oraz przebieg dochodzenia medycznego przyczyny wypadku. Wskazano niebezpieczeństwa związane z brakiem kwalifikacji medycznej płetwonurków – amatorów.

13

Detecting epileptogenesis in power variant domains

Lewis R. A., Parks B., Shmueli D., Capinst S.

Control and Cybernetics

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2011

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Vol. 40, no 2

293-314

EN

This paper presents the merging of two sets of experiments in the continuing endeavor to mine epileptiform activity from Electroencephalograms (EEG). The goal is to develop robust classification rules for identifying epileptiform activity in the human brain. We present advancements using the author's proprietary developed spectral analysis software to link power spectra of rat EEGs experiencing epilepsy seizures with the authors DFA algorithm and their MATLAB spectral analysis. Our system links 1) power spectra of seizures, in sleep, spike and seizure states, with 2) Deterministic Finite Automata (DFA). Combining power spectra with DFA to correctly predict and identify epileptiform activity (spikes) and epileptic seizures opens the door to creating classifiers for seizures. We also present a DFA that separates the states between seizure and nonseizure using robust testing and additional algorithms to increase the rigor when the methodology analyses noisy signals. Our results show optimal identification of seizures even when significant artifact and noise is present in the polyphonic domain. Herein we present a dual methodology that increases epileptoid identification in a noisy domain that links time and frequency domain components from MATLAB and proprietary software to clinical epileptiform activity.

14

Padaczka - aspekt terapeutyczny, socjalny i ekonomiczny

Plech A.

Journal of Ecology and Health

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2010

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R. 14, nr 4

205-211

PL

Padaczka jest zespołem chorobowym manifestującym się zaburzeniami czynności mózgu, często występującymi u chorego w formie napadów drgawek i zaburzeń świadomości w różnych odstępach czasu. Znane są różne jej postacie kliniczne. Istotnym czynnikiem w patogenezie padaczki jest zwiększona, nienormalna pobudliwość neuronów mózgu powodująca wystąpienie nieprawidłowych, synchronicznych wyładowań elektrycznych określonych grup neuronów mózgu. Występowanie padaczki w populacji ludzkiej jest oceniane na 0,5 – 1%. W Polsce liczba chorych na padaczkę jest szacowana na 300 – 400 tys. osób, co uzasadnia uznanie jej za chorobę społeczną. Sytuacja społeczna chorych na padaczkę w różnych krajach, także w Polsce jest gorsza niż chorych na inne choroby przewlekle. Na ogół w krajach o niskim poziomie rozwoju cywilizacyjnego jest gorsza w porównaniu z krajami rozwiniętymi. Farmakoterapia stanowi istotną część leczenia padaczki. Stosowane leki przeciwpadaczkowe są lekami objawowymi, hamującymi drgawki, główny objaw choroby. Poważnym problemem farmakoterapii padaczki jest oporność na leki przeciwpadaczkowe, występująca u 30% chorych na padaczkę. Koszt leczenia chorego na padaczkę zarówno w Polsce jak i w innych krajach jest wyższy w porównaniu z przeciętnym kosztem leczenia osoby z ogólnej populacji społeczeństwa.

EN

Elepsies have been present thoroughout recorded history. The essential factor in pathogenesis of epilepsy is abnormal neuronal excitability in the brain, which results in excessive and uncontrolled discharge of group of neurons which spreads to other neurons and parts of the brain. These neuronal disturbances may induce seizures and unconsciousness of the patient. This syndrome consists of several, different clinical entities. The incidence of epilepsy occurs in about 0,5 – 1% of human population. The number of epileptic patients in Poland is valuated on 300 – 400 thousand people. Social situation of epileptics is worse than other non-epileptic patients. It is much worse in an undeveloped countries in comparison to well- developed societies. Pharmacotherapy plays an essential role in the treatment of epilepsy. Antiepileptic drugs prevent or decrease, high – frequency, repetitive neuronal firing and inhibit transmission epileptiform activity from epileptic focus to other parts of the brain. About 30% of epileptic patients is resistant to antiepileptic drugs in spite of proper therapy. This phenomenon is of great importance from the therapeutical and economical point of view. The cost of epileptic patient in Poland as well as in other countries is much higher in comparison to other non-epileptic patients.

15

Epileptic burst measurement using microelectrodes equipped on a cryogenic microprobe for minimally invasive brain surgery of intractable epilepsy treatment

Yamakawa T., Yamakawa T., Ishizuka S., Grigorievich Z. L., Suzuki M., Fujii M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2009

|

Vol. 3, No. 4

76-79

EN

A microprobing system, which has the functions of measuring the intracranial EEG(IC-EEG) and freezing brain tissue, is proposed for the minimally invasive brain cryogenic surgery of intractable epilepsy treatment. Two fi76 µm platinum electrodes were equipped on a fi0.8 mm cryogenic probe. Epileptic burst, which was evoked on a brain sample of a rat, was measured by the electrodes. The freezing function was confirmed with the experiments with sliced hippocampus samples of a rat.

16

Comparison of different SPECT images analysis methods for inter-hemispheric asymmetry detection in patients with epileptic symptoms

Olejarczyk E., Przytulska M., Bajera A., Królicki L.

Biocybernetics and Biomedical Engineering

|

2008

|

Vol. 28, no. 4

33-41

EN

The aim of this work was examination of asymmetries in activity of the left and right cerebral hemispheres as well as localization and contouring of the regions of reduced or increased activity on the basis of single photon emission computed tomography (SPECT) images. The mean and standard deviation of normalized intensities inside the contoured areas of images, entropy based on intensity histograms and Chen's fractal dimension were calculated.

17

Bistability in epileptic phenomena

Suffczyński P., Kalitzin S., da Silva F. L.

Bio-Algorithms and Med-Systems

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2005

|

Vol. 1, no. 1/2

259--266

EN

It is currently believed that the mechanisms underlying spindle oscillations are related to those that generate spike and wave (SW) discharges. The mechanisms of transition between these two types of activity, however, are not well understood. In order to provide more insight into the dynamics of the neuronal networks leading to seizure generation in a rat experimental model of absence epilepsy we developed a computational model of thalamo-cortical circuits based on relevant (patho)physiological data. The model is constructed at the macroscopic level since this approach allows to investigate dynamical properties of the system and the role played by different mechanisms in the process of seizure generation, both at short and long time scales. The main results are the following: (i) SW discharges represent dynamical bifurcations that occur in a bistable neuronal network. (ii) The durations of paroxysmal and normal epochs have exponential distributions, indicating that transitions between these two stable states occur randomly over time with constant probabilities. (iii) The probabilistic nature of the onset of paroxysmal activity implies that it is not possible to predict its occurrence. (iv) The bistable nature of the dynamical system allows an ictal state to be aborted by a single counter-stimulus.

PL

Mechanizm spontanicznego powstawania oraz wygaszania napadów epileptycznych nie jest dobrze poznany. W celu lepszego zrozumienia tych mechanizmów opracowany został model symulacyjny układu wzgórzowo-korowego, odpowiedzialnego za powstawanie napadów nieświadomości u ludzi oraz zwierząt. Model wykorzystuje podejście populacyjne, co pozwoliło na zbadanie właściwości układu w skali krótko- i długoczasowej. Główne wyniki pracy to: (i) napady nieświadomości powstają w bistabilnej sieci neuronalnej, (ii) długości napadów oraz odcinków pomiędzy napadami mają rozkład eksponencjalny, co sugeruje, że przejścia pomiędzy stanem normalnym a napadem odbywają się losowo w czasie ze stałym prawdopodobieństwem (iii) probabilistyczna natura powstawania napadów sugeruje, że nie jest możliwe ich przewidywanie (iv) napady powstające w układzie bistabilnym mogą być zatrzymane poprzez stymulację pojedynczym impulsem elektrycznym.